With the emergence of new communication techniques, different types of mobile and fixed terminals capable of multimedia communication have been developed for enabling users to consume multimedia services. New services involving communication of various types of media, are also constantly being developed for terminal users to increase the field of usage for their communication terminals. In the following text, the term “user services” generally represents any type of services that can be activated for a user of a communication terminal. User services are thus somehow related to the user, e.g. services depending on the user's geographic position, terminal type, use of address lists, and so forth.
Recently, solutions have been devised for creating and offering relevant and potentially attractive services that have been adapted to different service consumers according to their interests and needs in different situations. These user services can thus be customised for individual users depending on their user profiles and/or current situation. Some examples are advertisements and personalised TV. Solutions have also been suggested for managing groups or “clusters” of users with similar behaviours, and for adapting various user services to the common characteristics of these user groups. WO 06/115442 (Ericsson) discloses a mechanism where the particular needs of a user group can be met by providing relevant context information that has been adapted to particular interests and needs of the group.
Differentiated adaptation of services for users and user groups may depend on and require that information on the users' profile, current situation, as well as earlier behaviour and habits, is available to the service providers in a useful manner. This kind of information can be extracted from different sources, typically traffic data available in communication networks, i.e. information on executed calls and other sessions such as SMS (Short Message Service), MMS (Multimedia Message Service), IMS sessions, and so forth, using various data mining techniques which have been developed recently. For example, so-called “Machine Learning” (ML) algorithms and tools can be used for the extraction of relevant and useful information on the users from the available traffic data, which may be utilised by operators when developing and introducing new services.
Great amounts of traffic data are thus generally available from Charging Data Records (CDR) which are usually generated and stored for the networks to support charging for executed calls and sessions. Traffic data can also be obtained by means of various traffic analysing devices, such as Deep Packet Inspection (DPI) analysers, which can be installed at communication nodes in the network. Further, the CDR data may be generated from DPI data in some cases. The traffic may involve various communications services that can be detected in this way, such as voice, SMS, MMS, peer-to-peer services, downloading, streaming, and so forth.
A Data Mining Engine (DME) may further be employed that collects traffic data and extracts user information therefrom using various data mining and machine learning algorithms. The DME may even be used to obtain information on the social relations between different users, and even the “strength” of those relations, depending on the amount and type of communications these users have conducted with each other as well as time of day, duration and location when making their calls and sessions.
However, the above-described data mining and machine learning algorithms typically provide rather “raw” output data which can be difficult to interpret and understand for different receiving parties such as service providers or the like, either external or internal. The traffic data may also originate from different communication techniques producing different outcomes from the algorithms above. In addition, no useful universal and consistent “language” has yet been defined and established to describe, e.g., different types of service usage and social relations in a uniform or standardised manner understandable for any data receiving parties. As a result, the output data from a DME of today may well be interpreted differently by different receiving parties, and/or may not even be properly understood at all or interpreted inaccurately.
The DME data is also often presented in communication technology specific terms requiring special knowledge to understand. It is therefore not unusual to employ experts skilled in data mining and communication techniques in order to interpret, process and describe the DME output data correctly. These persons should thus be very accomplished in interpreting data mining results as well as in behavioural science, among other things. Employing such experts may be costly or not even possible. Still further, different experts may describe the DME output data in different ways with inconsistent terms.
FIG. 1 illustrates an example of how data mining can be employed for a communication network, according to the prior art. A data mining engine (DME) 100 comprises various machine learning algorithms (MLA:s) 100a which are used for processing traffic data (TD) provided from a so-called “Mining Object Repository” (MOR) 102. The MOR 102 collects CDR information and DPI information, either intermittently or on a more continuous basis, from the network in the manner described above. After processing the traffic data, the DME 100 provides raw output data to a plurality of third parties 104 (A,B,C . . . ), e.g. different service providers, and such DME data may be difficult to interpret and use, as explained above. It is thus up to the receiving third parties 104 how the output data from the DME 100 is interpreted and used, e.g. in terms of user profiles, social networks, user segments, and so forth, which may require considerable efforts and skills.